Oil price prediction using ensemble machine learning

Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last couple of decades, both academicians and practitioners devoted proactive knowledge to address this issue. A strand of them has focused on some key factors that may influence the crude oil price prediction accuracy. This paper extends this particular branch of recent works by considering a number of influential features as inputs to test the forecasting performance of daily WTI crude oil price covering the period 4th January 1999 through 10th October 2012. Empirical results indicate that the proposed methods are efficient and warrant further research in this field.